T 38: Data analysis, information technology II
Tuesday, March 16, 2021, 16:00–18:30, Tm
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16:00 |
T 38.1 |
Composition Study of Cosmic Rays with IceCube Observa-tory using Graph Neural Networks — •Paras Koundal for the IceCube collaboration
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16:15 |
T 38.2 |
Deep-Learning-Based Reconstruction of Cosmic-Ray Properties From Extensive Air Shower Measurements — Martin Erdmann, Jonas Glombitza, Berenika Idaszek, and •Niklas Langner
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16:30 |
T 38.3 |
Using a conditional Invertible Neural Network to determine the parameters of ultra-high-energy cosmic ray sources — Teresa Bister, Martin Erdmann, and •Josina Schulte
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16:45 |
T 38.4 |
Muon bundle reconstruction with KM3NeT/ORCA using graph convolutional networks — •Stefan Reck for the ANTARES-KM3NeT-Erlangen collaboration
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17:00 |
T 38.5 |
CNN classification and regression for ANTARES — •Nicole Geißelbrecht for the ANTARES-KM3NeT-Erlangen collaboration
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17:15 |
T 38.6 |
graFEI: Full Event Interpretation using Graph Neural Networks at Belle II — •Lea Reuter, James Kahn, Ilias Tsaklidis, and Pablo Goldenzweig
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17:30 |
T 38.7 |
Pixel Detector Background Generation using Generative Adversarial Networks at Belle II — •Hosein Hashemi, Thomas Kuhr, Martin Ritter, Nikolai Hartman, and Matei Srebre
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17:45 |
T 38.8 |
GANplifying Event Samples — Anja Butter, •Sascha Diefenbacher, Gregor Kasieczka, Benjamin Nachman, and Tilman Plehn
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18:00 |
T 38.9 |
Fast Simulation of High Granularity Calorimeters with Deep Generative Models — •Peter McKeown
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18:15 |
T 38.10 |
Optimization of Selective Background Monte Carlo Simulation with Graph Neural Networks at Belle II — •Boyang Yu, Thomas Kuhr, and Nikolai Hartmann
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